Research Papers: Design Theory and Methodology

Biased Information Passing Between Subsystems Over Time in Complex System Design

[+] Author and Article Information
Jesse Austin-Breneman

Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: jausbren@umich.edu

Bo Yang Yu

Department of Mechanical Engineering,
Massachusetts Institute of Technology,
Cambridge, MA 02139
e-mail: byyu@mit.edu

Maria C. Yang

Department of Mechanical Engineering,
Massachusetts Institute of Technology,
Cambridge, MA 02139
e-mail: mcyang@mit.edu

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 27, 2015; final manuscript received September 28, 2015; published online November 4, 2015. Assoc. Editor: Kristina Shea.

J. Mech. Des 138(1), 011101 (Nov 04, 2015) (9 pages) Paper No: MD-15-1171; doi: 10.1115/1.4031745 History: Received February 27, 2015; Revised September 28, 2015

During the early stage design of large-scale engineering systems, design teams are challenged to balance a complex set of considerations. The established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice suboptimal system-level results are often reached due to factors such as satisficing, ill-defined problems, or other project constraints. Twelve subsystem and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate subsystems in their own work. Responses showed subsystem team members often presented conservative, worst-case scenarios to other subsystems when negotiating a tradeoff as a way of hedging against their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding “margins,” is modeled in this paper with a series of optimization simulations. Three “bias” conditions were tested: no bias, a constant bias, and a bias which decreases with time. Results from the simulations show that biased information passing negatively affects both the number of iterations needed and the Pareto optimality of system-level solutions. Results are also compared to the interview responses and highlight several themes with respect to complex system design practice.

Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.


de Weck, O. L. , and Jones, M. B. , 2006, “ Isoperformance: Analysis and Design of Complex Systems With Desired Outcomes,” Syst. Eng., 9(1), pp. 45–61. [CrossRef]
Simon, H. A. , 1973, “ The Structure of Ill Structured Problems,” Artif. Intell., 4(3–4), pp. 181–201. [CrossRef]
McGowan, A. M. R. , Daly, S. , Baker, W. , Papalambros, P. , and Seifert, C. , 2013, “ A Socio-Technical Perspective on Interdisciplinary Interactions During the Development of Complex Engineered Systems,” Procedia Comput. Sci., 16, pp. 1142–1151. [CrossRef]
Simpson, T. W. , Poplinski, J. D. , Koch, P. N. , and Allen, J. K. , 2001, “ Metamodels for Computer-Based Engineering Design: Survey and Recommendations,” Eng. Comput., 17(2), pp. 129–150. [CrossRef]
Sobieszczanski-Sobieski, J. , and Haftka, R. T. , 1997, “ Multidisciplinary Aerospace Design Optimization: Survey of Recent Developments,” Struct. Multidiscip. Optim., 14(1), pp. 1–23. [CrossRef]
Vincent, T. L. , 1983, “ Game Theory as a Design Tool,” J. Mech. Transm. Autom. Des., 105(2), pp. 165–170. [CrossRef]
Lewis, K. , 1996, “ An Algorithm for Integrated Subsystem Embodiment and System Synthesis,” Ph.D. thesis, Georgia Institute of Technology, Atlanta, GA.
Whitfield, R. I. , Duffy, R. I. , Coates, G. , and Hills, W. , 2002, “ Distributed Design Coordination,” Res. Eng. Des., 13(4), pp. 243–252.
Hazelrigg, G. A. , 1998, “ A Framework for Decision-Based Engineering Design,” ASME J. Mech. Des., 120(4), pp. 653–658. [CrossRef]
Chanron, V. , and Lewis, K. , 2005, “ A Study of Convergence in Decentralized Design Processes,” Res. Eng. Des., 16(3), pp. 133–145. [CrossRef]
Chanron, V. , Singh, T. , and Lewis, K. , 2005, “ Equilibrium Stability in Decentralized Design Systems,” Int. J. Syst. Sci., 36(10), pp. 651–662. [CrossRef]
Xiao, A. , Zheng, S. , Allen, J. K. , Rosen, D. W. , and Mistree, F. , 2005, “ Collaborative Multidisciplinary Decision Making Using Game Theory and Design Capability Indices,” Res. Eng. Des., 16(1–2), pp. 57–72. [CrossRef]
Gurnani, A. P. , and Lewis, K. , 2008, “ Using Bounded Rationality to Improve Decentralized Design,” AIAA J., 46(12), pp. 3049–3059. [CrossRef]
Lewis, K. E. , Chen, W. , and Schmidt, L. C. , 2006, Decision Making in Engineering Design, American Society of Mechanical Engineers, New York.
Nash, J. F. , 1951, “ Non-Cooperative Games,” Ann. Math., 54(2), pp. 286–295. [CrossRef]
Martins, J. R. R. , and Lambe, A. , 2012, “ Multidisciplinary Design Optimization: A Survey of Architectures,” AIAA J., 51(9), pp. 1–53.
Takamatsu, T. , Hashimoto, I. , and Ohno, H. , 1970, “ Optimal Design of a Large Complex System From the Viewpoint of Sensitivity Analysis,” Ind. Eng. Chem. Process Des. Dev., 9(3), pp. 368–379. [CrossRef]
Thunnissen, D. P. , 2004, “ Method for Determining Margins in Conceptual Design,” J. Spacecr. Rockets, 41(1), pp. 85–91. [CrossRef]
Eckert, C. M. , Isaksson, O. , and Earl, C. F. , 2014, “ Design Margins as a Key to Understanding Design Iterations,” ASME Paper No. DETC2014-34275.
Sentz, K. , and Ferson, S. , 2011, “ Probabilistic Bounding Analysis in the Quantification of Margins and Uncertainties,” Reliab. Eng. Syst. Saf., 96(9), pp. 1126–1136. [CrossRef]
Helton, J. , 2011, “ Quantification of Margins and Uncertainties: Conceptual and Computational Basis,” Reliab. Eng. Syst. Saf., 96(9), pp. 976–1013. [CrossRef]
Yi, S. , Shin, J. , and Park, G. J. , 2008, “ Comparison of MDO Methods With Mathematical Examples,” Struct. Multidiscip. Optim., 35(5), pp. 391–402. [CrossRef]
Honda, T. , Ciucci, F. , Lewis, K. , and Yang, M. , 2010, “ A Comparison of Information Passing Strategies in System Level Modeling,” ASME Paper No. DETC2010-29026.
Gu, X. , Renaud, J. , Batill, S. M. , Brach, R. M. , and Budhiraja, A. S. , 2000, “ Worst Case Propagated Uncertainty of Multidisciplinary Systems in Robust Design Optimization,” Struct. Multidiscip. Optim., 20(3), pp. 190–213. [CrossRef]
Ciucci, F. , Honda, T. , and Yang, M. C. , 2012, “ An Information-Passing Strategy for Achieving Pareto Optimality in the Design of Complex Systems,” Res. Eng. Des., 23(1), pp. 71–83. [CrossRef]
Collopy, P. , 2001, “ Economic-Based Distributed Optimal Design,” AIAA Paper No. 2001-4675.
Lewis, K. , and Mistree, F. , 1997, “ Modeling Interactions in Interdisciplinary Design: A Game Theoretic Approach,” AIAA J., 35(8), pp. 1387–1392. [CrossRef]
Kalsi, M. , Hacker, K. , and Lewis, K. , 2001, “ A Comprehensive Robust Design Approach for Decision Trade-Offs in Complex Systems Design,” ASME J. Mech. Des., 123(1), pp. 1–10. [CrossRef]
Simon, H. A. , 1997, Models of Bounded Rationality, MIT Press, Cambridge, MA.
Smith, R. P. , and Eppinger, S. D. , 1997, “ Identifying Controlling Features of Engineering Design Iteration,” Manage. Sci., 43(3), pp. 276–293. [CrossRef]
Yassine, A. , and Braha, D. , 2003, “ Complex Concurrent Engineering and the Design Structure Matrix Approach,” Concurrent Eng.: Res. Appl., 11(3), pp. 165–177. [CrossRef]
Yassine, A. , Joglekar, N. , Braha, D. , Eppinger, S. , and Whitney, D. , 2003, “ Information Hiding in Product Development: The Design Churn Effect,” Res. Eng. Des., 14(3), pp. 145–161. [CrossRef]
Klein, M. , Sayama, H. , Faratin, P. , and Bar-Yam, Y. , 2003, “ The Dynamics of Collaborative Design: Insights From Complex Systems and Negotiation Research,” Concurrent Eng., 11(3), pp. 201–209. [CrossRef]
Di Marco, M. K. , Taylor, J. E. , and Alin, P. , 2010, “ Emergence and Role of Cultural Boundary Spanners in Global Engineering Project Networks,” J. Manage. Eng., 26(3), pp. 123–132. [CrossRef]
Minneman, S. L. , and Leifer, L. J. , 1993, “ Group Engineering Design Practice: The Social Construction of a Technical Reality,” International Conference on Engineering Design (ICED), Vol. 93, pp. 301–310.
Nardi, B. , and Whittaker, S. , 2002, “ The Place of Face-to-face Communication in Distributed Work,” Distributed Work, P. Hinds , and S. Keisler , eds., MIT Press, Cambridge, MA, pp. 83–109.
Kendon, A. , 1990, Conducting Interaction: Patterns of Behavior in Focused Encounters, Cambridge University Press, New York.
Cooke, N. J. , and Gorman, J. C. , 2006, “ Assessment of Team Cognition,” International Encyclopedia of Ergonomics and Human Factors, P. Karwowski , ed., Taylor & Francis Ltd., Boca Raton, FL, pp. 270–275.
Minneman, S. , Harrison, S. , Janssen, B. , Kurtenbach, G. , Moran, T. , Smith, I. , and van Melle, B. , 1995, “ A Confederation of Tools for Capturing and Accessing Collaborative Activity,” Third ACM International Conference on Multimedia, pp. 523–534.
Coello Coello, C. A. , Lamont, G. B. , and van Veldhuizen, D. A. , 2007, Evolutionary Algorithms for Solving Multi-Objective Problems, Springer, New York.
Deb, K. , Thiele, L. , Laumanns, M. , and Zitzler, E. , 2005, Scalable Test Problems for Evolutionary Multiobjective Optimization, Springer, London.
Brown, N. F. , and Olds, J. R. , 2006, “ Evaluation of Multidisciplinary Optimization Techniques Applied to a Reusable Launch Vehicle,” J. Spacecr. Rockets, 43(6), pp. 1289–1300. [CrossRef]
Honda, T. , Ciucci, F. , Lewis, K. , and Yang, M. C. , 2010, “ A Comparison of Information Passing Strategies in System Level Modeling,” ASME Paper No. DETC2010-29026.
Honda, T. , Ciucci, F. , Lewis, K. , and Yang, M. C. , 2015, “ Comparison of Information Passing Strategies in System-Level Modeling,” AIAA J., 53(5), pp. 1121–1133. [CrossRef]
Braha, D. , and Yaneer, B. , 2007, “ The Statistical Mechanics of Complex Product Development: Empirical and Analytical Results,” Manage. Sci., 53(7), pp. 1127–1145. [CrossRef]


Grahic Jump Location
Fig. 10

Number of iterations for asymmetric decreasing bias conditions

Grahic Jump Location
Fig. 7

Normalized distance to Pareto frontier for asymmetric constant bias conditions

Grahic Jump Location
Fig. 8

Normalized distance to Pareto frontier for asymmetric decreasing bias conditions

Grahic Jump Location
Fig. 9

Number of iterations for asymmetric constant bias conditions

Grahic Jump Location
Fig. 2

Normalized distance to the Pareto frontier for all three test conditions

Grahic Jump Location
Fig. 3

Average number of system iterations for all three test conditions

Grahic Jump Location
Fig. 4

Solution path in the no bias condition b = 0

Grahic Jump Location
Fig. 1

System schematic for one iteration

Grahic Jump Location
Fig. 5

Solution path in the static bias condition b = 1.3

Grahic Jump Location
Fig. 6

Solution path in the decreasing bias condition b=1.3−.1*i



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In